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Limitations and future work

Chapter 6: Conclusions and Limitations

6.2 Limitations and future work

This work implements high-throughput computing to systematically investigate the torsional properties of three types of chiral nanofibres and their bundles’ structure with various enantiomer configurations. There are several limitations of this research, and many aspects of the high-throughput process need to be improved.

x The accuracy of the empirical force field for describing the nanomaterials directly determines whether the results obtained are reliable or not. Currently, the AIREBO potential used in the simulation can only provide qualitative quantities of most carbon nanothreads. Thus, for high-throughput modelling, accurate potentials for all the nanocarbon materials need to be developed.

x The materials studied are free structures, while in the real world, defect-free nanofibres can hardly be obtained. Therefore, investigating the effects of the existence of the defects will provide great references for their applications.

x This research only investigates the torsional properties of three nanofibres.

There is still a large state space for the properties of the nanofibres with various configurations not yet explored. Thus, a high-throughput framework needs to be developed to conduct the simulations and process the data.

x As the state space of the properties is extremely large, it is infeasible to conduct simulations for all the models. To find the best way of designing the materials to meet certain requirements, active learning methods need to be developed to guide high-throughput computing to reach the targets with minimum effort.

x To verify the findings of the simulations, experimental works should be conducted in future. The feedback information will help to improve the accuracy of the intelligent learning methods.

Bibliography

1. Benzene-derived carbon nanothreads. Nat. Mater., 2015. 14: p. 43.

2. Xu, E.-s., P.E. Lammert, and V.H. Crespi, Systematic Enumeration of sp3 Nanothreads. Nano Letters, 2015. 15(8): p. 5124-5130.

3. Iijima, S., Helical Microtubules of Graphitic Carbon. Nature, 1991. 354(6348):

p. 56-58.

4. Qian, Dong, et al., Mechanics of carbon nanotubes. Applied Mechanics Reviews, 2002. 55(6): p. 495-533.

5. Ruoff, R.S., D. Qian, and W.K. Liu, Mechanical properties of carbon nanotubes: theoretical predictions and experimental measurements. Comptes Rendus Physique, 2003. 4(9): p. 993-1008.

6. Kang, I., et al., Introduction to carbon nanotube and nanofiber smart materials.

Composites Part B: Engineering, 2006. 37(6): p. 382-394.

7. Areshkin, D.A., et al., Electronic properties of diamond clusters: self-consistent tight binding simulation. Diamond and Related Materials, 2004.

13(10): p. 1826-1833.

8. Huang, Y., J. Wu, and K.C. Hwang, Thickness of graphene and single-wall carbon nanotubes. Physical Review B, 2006. 74(24): p. 245413.

9. Pine, P., Y.E. Yaish, and J. Adler, Vibrational analysis of thermal oscillations of single-walled carbon nanotubes under axial strain. Physical Review B, 2014. 89(11): p. 115405.

10. Muñoz, E., J. Lu, and B.I. Yakobson, Ballistic Thermal Conductance of Graphene Ribbons. Nano Letters, 2010. 10(5): p. 1652-1656.

11. Chowdhury, R., et al., Transverse vibration of single-layer graphene sheets.

Journal of Physics D: Applied Physics, 2011. 44(20): p. 205401.

12. Overney, G., W. Zhong, and D. Tománek, Structural rigidity and low frequency vibrational modes of long carbon tubules. Zeitschrift für Physik D Atoms, Molecules and Clusters, 1993. 27(1): p. 93-96.

13. Gao, G., T. Çagin, and W.A. Goddard, Energetics, structure, mechanical and vibrational properties of single-walled carbon nanotubes. Nanotechnology, 1998. 9(3): p. 184-191.

14. Yakobson, B.I., C.J. Brabec, and J. Bernholc, Nanomechanics of Carbon Tubes:

Instabilities beyond Linear Response. Physical Review Letters, 1996. 76(14):

p. 2511-2514.

15. Srivastava, D., M. Menon, and K. Cho, Nanoplasticity of Single-Wall Carbon Nanotubes under Uniaxial Compression. Physical Review Letters, 1999.

83(15): p. 2973-2976.

16. Salvetat, J.-P., et al., Mechanical properties of carbon nanotubes. 1999. 69(3):

p. 255-260.

17. Chang, T., Torsional behavior of chiral single-walled carbon nanotubes is loading direction dependent. Applied Physics Letters, 2007. 90(20): p. 201910.

18. Hill, F.A., T.F. Havel, and C. Livermore, Modeling mechanical energy storage in springs based on carbon nanotubes. Nanotechnology, 2009. 20(25): p.

255704.

19. Zhang, R., et al., Superstrong Ultralong Carbon Nanotubes for Mechanical Energy Storage. 2011. 23(30): p. 3387-3391.

20. Kim, S.H., et al., Harvesting electrical energy from carbon nanotube yarn twist.

Science, 2017. 357(6353): p. 773.

21. Dambone Sessa, S., et al., Li-Ion Battery-Flywheel Hybrid Storage System:

Countering Battery Aging During a Grid Frequency Regulation Service. 2018.

8(11): p. 2330.

22. Genta, G., 2 - Application of flywheel energy storage systems, in Kinetic Energy Storage, G. Genta, Editor. 1985, Butterworth-Heinemann. p. 27-46.

23. Ren, Y., et al., Fatigue Behaviour of Unidirectional Single-Walled Carbon Nanotube Reinforced Epoxy Composite under Tensile Load. 2003. 12(1): p.

096369350301200103.

24. Fitzgibbons, T.C., et al., Benzene-derived carbon nanothreads. Nature Materials, 2014. 14: p. 43.

25. Juhl, S.J., et al., Local Structure and Bonding of Carbon Nanothreads Probed by High-Resolution Transmission Electron Microscopy. Journal of the American Chemical Society, 2019. 141(17): p. 6937-6945.

26. Thermal conductivity of a new carbon nanotube analogue: the diamond nanothread. Carbon, 2016. 98: p. 232.

27. Boron-graphdiyne: a superstretchable semiconductor with low thermal conductivity and ultrahigh capacity for Li, Na and Ca ion storage. J. Mater.

Chem., 2018. 6: p. 11022.

28. Roman, R.E., K. Kwan, and S.W. Cranford, Mechanical Properties and Defect Sensitivity of Diamond Nanothreads. Nano Letters, 2015. 15(3): p. 1585-1590.

29. Zhan, H., et al., From brittle to ductile: a structure dependent ductility of diamond nanothread. Nanoscale, 2016. 8(21): p. 11177-11184.

30. Zhan, H., et al., The morphology and temperature dependent tensile properties of diamond nanothreads. Carbon, 2016. 107: p. 304-309.

31. Zhan, H. and Y. Gu, Chapter 7 - Thermal Conductivity of Diamond Nanothread, in Thermal Transport in Carbon-Based Nanomaterials, G. Zhang, Editor. 2017, Elsevier. p. 185-204.

32. Zhan, H., et al., The best features of diamond nanothread for nanofibre applications. Nature Communications, 2017. 8: p. 14863.

33. Saha, B. and A. Datta, Reactive Molecular Dynamics Simulations of Self-Assembly of Polytwistane and Its Application for Nanofibers. The Journal of Physical Chemistry C, 2018. 122(33): p. 19204-19211.

34. Silveira, J.F.R.V. and A.R. Muniz, First-principles calculation of the mechanical properties of diamond nanothreads. Carbon, 2017. 113: p. 260-265.

35. Gryn’ova, G. and C. Corminboeuf, Topology-Driven Single-Molecule Conductance of Carbon Nanothreads. The Journal of Physical Chemistry Letters, 2019. 10(4): p. 825-830.

36. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys., 1995. 117: p. 1.

37. Lee, J.G., Computational Materials Science. 2017, Boca Raton: CRC Press.

38. Ni, B. and S.B. Sinnott, Tribological properties of carbon nanotube bundles predicted from atomistic simulations. Surface Science, 2001. 487(1): p. 87-96.

39. Tang, C.Y., L. Zhang, and K. Mylvaganam, Mechanical Properties of a Silicon Nano-Wire Under Uni-Axial Tension and Compression. Journal of Computational and Theoretical Nanoscience, 2010. 7(10): p. 2135-2143.

40. Faria, B., N. Silvestre, and J.N. Canongia Lopes, Tension–twisting dependent kinematics of chiral CNTs. Composites Science and Technology, 2013. 74: p.

211-220.

41. Zhang, H., et al., Stiffness-dependent interlayer friction of graphene. Carbon, 2015. 94: p. 60-66.

42. Xia, Z.H., P.R. Guduru, and W.A. Curtin, Enhancing Mechanical Properties of Multiwall Carbon Nanotubes via $s{p}^{3}$ Interwall Bridging. Physical Review Letters, 2007. 98(24): p. 245501.

43. Barzegar, H.R., et al., C60/Collapsed Carbon Nanotube Hybrids: A Variant of Peapods. Nano Letters, 2015. 15(2): p. 829-834.

44. A second-generation reactive empirical bond order (REBO) potential energy expression for hydrocarbons. J. Phys.: Condens. Matter, 2002. 14(4): p. 783.

45. Stuart, S.J., A.B. Tutein, and J.A. Harrison, A reactive potential for hydrocarbons with intermolecular interactions. Journal of Chemical Physics, 2000. 112(14): p. 6472-6486.

46. Kolmogorov, A.N. and V.H. Crespi, Smoothest Bearings: Interlayer Sliding in Multiwalled Carbon Nanotubes. Physical Review Letters, 2000. 85(22): p.

4727-4730.

47. Ouyang, W., et al., Nanoserpents: Graphene Nanoribbon Motion on Two-Dimensional Hexagonal Materials. Nano Letters, 2018. 18(9): p. 6009-6016.

48. Kolmogorov, A.N. and V.H. Crespi, Registry-dependent interlayer potential for graphitic systems. Physical Review B, 2005. 71(23): p. 235415.

49. Nosé, S., A unified formulation of the constant temperature molecular dynamics methods. 1984. 81(1): p. 511-519.

50. Hoover, W.G., Canonical dynamics: Equilibrium phase-space distributions.

Physical Review A, 1985. 31(3): p. 1695-1697.

51. Tildesley, M.P.A.a.D.J., Computer Simulation of Liquids. Second ed. 2017, Oxford Scholarship Online.

52. E. Braun, J.G., H.B. Mayes, D.L. Mobley, J.I. Monroe, S. Prasad, D.M.

Zuckerman, Best practices for foundations in molecular simulations [Article v1.0]. Living J. Comput. Mol. Sc, 2018. 1 (1).

53. Andrew I. Jewett, Z.Z., Joan-Emma Shea, Moltemplate a Coarse-Grained Model Assembly Tool. Biophysical Journal, 2013. 104(2): p. 169a.

54. Martínez, L., et al., PACKMOL: A package for building initial configurations for molecular dynamics simulations. 2009. 30(13): p. 2157-2164.

55. Correa-Baena, J.-P., et al., Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing. Joule, 2018. 2(8): p. 1410-1420.

56. Chen, W., High-Throughput Computing for Accelerated Materials Discovery, in Computational Materials System Design, D. Shin and J. Saal, Editors. 2018, Springer International Publishing: Cham. p. 169-191.

57. Nosengo, N., Can artificial intelligence create the next wonder material?

Nature, 2016. 533: p. 22-25.

58. Butler, K.T., et al., Machine learning for molecular and materials science.

Nature, 2018. 559(7715): p. 547-555.

59. Humphrey, W., A. Dalke, and K. Schulten, VMD: Visual molecular dynamics.

Journal of Molecular Graphics, 1996. 14(1): p. 33-38.

60. Li, J., AtomEye: an efficient atomistic configuration viewer. Modelling and Simulation in Materials Science and Engineering, 2003. 11(2): p. 173-177.

61. Stukowski, A., Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool. Modelling and Simulation in Materials Science and Engineering, 2009. 18(1): p. 015012.

62. WL., D., PyMOL: an open‐source molecular graphics tool. Ccp4 Newslett Protein Crystallogr, 2002. 40: 11.

63. McKinney, W.P.D.T., pandas: powerful Python data analysis toolkit. 2011.

64. Hunter, J.D., Matplotlib: A 2D Graphics Environment. 2007. 9(3): p. 90-95.

65. Geng, J. and T. Chang, Nonlinear stick-spiral model for predicting mechanical behavior of single-walled carbon nanotubes. Physical Review B, 2006. 74(24):

p. 245428.

Appendices

Appendix A: The enumeration program.

Figure A-1: Python code for bundle structure enumeration

Appendix B: The bond breaking detection program.

Figure A-2: LAMMPS code for bond breaking detection

Appendix C: Gravimetric densities and twist strain rate of nanofibre bundles.

Figure A-3: Gravimetric energy density densities and twist strain rate of PT bundles

Figure A-4: Gravimetric energy densities and twist strain rate of stiff-chiral-3 NTH bundles

Figure A-5: Gravimetric Energy densities and twist strain rate of (8, 3) CNT bundles